A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Liu, Li; Wang, Qianru; Wang, Jianzhou; Liu, Ming

doi:10.1111/coin.12059

Cited by 68 publications

(32 citation statements)

References 42 publications

(52 reference statements)

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“…In this experiment, the listing technique was used to determine the number of input layers and hidden layers for the different ANN models [44]. From Figure 3 and Table 3, the following conclusions were obtained:…”

Section: Experiments I: Selection Of the Wind Speed Forecasting Modelmentioning

confidence: 98%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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Abstract:In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.

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Section: Experiments I: Selection Of the Wind Speed Forecasting Modelmentioning

confidence: 98%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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show abstract

“…Then we select four evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean error (ME), to evaluate the model performance more comprehensively. MAPE is a generally accepted metric for forecasting accuracy, and MAE and RMSE can measure the average magnitude of the forecast errors; however, RMSE imposes a greater penalty on a large error than several small errors [43].…”

Section: Model Evaluationmentioning

confidence: 99%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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“…The MAPE criteria are listed in Table 2 [55]. If the value of MAPE is smaller than 10%, the forecasting degree is excellent; if the value is between 10% and 20%, the forecasting degree is good; if the value is between 20% and 50%, the forecasting degree is reasonable; however, if the value is larger than 50%, the forecasting degree is incorrect, which indicates that the forecasting result is very poor.…”

Section: Evaluation Of Multiple Pointsmentioning

confidence: 99%

“…The models could be fitted by least squares regressions to find the values of the parameters, which could minimize the error term after p and q are set. Akaike information criterion (AICs) are applied to judge whether p and q are the best [55]. In our experiments, the form of ARIMA for both short-term wind speed and electricity price is ARIMA (3,2,1).…”

Section: Statistical Modelmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Cited by 68 publications

References 42 publications

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

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